Twenty years have passed since renowned Harvard Professor Larry Lessig coined the phrase “Code is Law”, suggesting that in the digital age, computer code regulates behavior much like legislative code traditionally did.These days, the computer code that powers artificial intelligence (AI) is a salient example of Lessig’s statement.
Good AI requires sound data.One of the principles,some would say the organizing principle, of privacy and data protection frameworks is data minimization.Data protection laws require organizations to limit data collection to the extent strictly necessary and retain data only so long as it is needed for its stated goal.
Preventing discrimination – intentional or not.
When is a distinction between groups permissible or even merited and when is it untoward? How should organizations address historically entrenched inequalities that are embedded in data? New mathematical theories such as “fairness through awareness” enable sophisticated modeling to guarantee statistical parity between groups.
Assuring explainability – technological due process.In privacy and freedom of information frameworks alike, transparency has traditionally been a bulwark against unfairness and discrimination.As Justice Brandeis once wrote, “Sunlight is the best of disinfectants.”
Deep learning means that iterative computer programs derive conclusions for reasons that may not be evident even after forensic inquiry.
Yet even with code as law and a rising need for law in code, policymakers do not need to become mathematicians, engineers and coders.Instead, institutions must develop and enhance their technical toolbox by hiring experts and consulting with top academics, industry researchers and civil society voices.Responsible AI requires access to not only lawyers, ethicists and philosophers but also to technical leaders and subject matter experts to ensure an appropriate balance between economic and scientific benefits to society on the one hand and individual rights and freedoms on the other hand.
It will be eons before AI thinks with a limbic brain, let alone has consciousness
AI programmes themselves generate additional computer programming code to fine-tune their algorithms—without the need for an army of computer programmers. In AI speak, this is now often referred to as “machine learning”.
An AI programme “catastrophically forgets” the learnings from its first set of data and would have to be retrained from scratch with new data. The website futurism.com says a completely new set of algorithms would have to be written for a programme that has mastered face recognition, if it is now also expected to recognize emotions. Data on emotions would have to be manually relabelled and then fed into this completely different algorithm for the altered programme to have any use. The original facial recognition programme would have “catastrophically forgotten” the things it learnt about facial recognition as it takes on new code for recognizing emotions. According to the website, this is because computer programmes cannot understand the underlying logic that they have been coded with.
Irina Higgins, a senior researcher at Google DeepMind, has recently announced that she and her team have begun to crack the code on “catastrophic forgetting”.
As far as I am concerned, this limbic thinking is “catastrophic thinking” which is the only true antipode to AI’s “catastrophic forgetting”. It will be eons before AI thinks with a limbic brain, let alone has consciousness.
Stephen Hawking warns artificial intelligence could end mankind
By Rory Cellan-JonesTechnology correspondent,2 December 2014
In 1995, psychologist and science journalist Daniel Goleman published a book introducing most of the world to the nascent concept of emotional intelligence. The idea–that an ability to understand and manage emotions greatly increases our chances of success–quickly took off, and it went on to greatly influence the way people think about emotions and human behavior.
What are my emotional strengths? What are my weaknesses?
How does my current mood affect my thoughts and decision making?
What’s going on under the surface that influences what others say or do?
2. You pause.
pausing helps you refrain from making a permanent decision based on a temporary emotion.
3. You strive to control your thoughts.
By striving to control your thoughts, you resist becoming a slave to your emotions, allowing yourself to live in a way that’s in harmony with your goals and values.
4. You benefit from criticism.
When you receive negative feedback, you keep your emotions in check and ask yourself: How can this make me better?
5. You show authenticity.
You know not everyone will appreciate your sharing your thoughts and feelings. But the ones who matter will.
6. You demonstrate empathy.
The ability to show empathy, which includes understanding others’ thoughts and feelings, helps you connect with others. Instead of judging or labeling others, you work hard to see things through their eyes.
Empathy doesn’t necessarily mean agreeing with another person’s point of view. Rather, it’s about striving to understand–which allows you to build deeper, more connected relationships.
To say that someone is or is not intelligent has never been merely a comment on their mental faculties. It is always also a judgment on what they are permitted to do. Intelligence, in other words, is political.
The problem has taken an interesting 21st-century twist with the rise of Artificial Intelligence (AI).
The term ‘intelligence’ itself has never been popular with English-language philosophers. Nor does it have a direct translation into German or ancient Greek, two of the other great languages in the Western philosophical tradition. But that doesn’t mean philosophers weren’t interested in it. Indeed, they were obsessed with it, or more precisely a part of it: reason or rationality. The term ‘intelligence’ managed to eclipse its more old-fashioned relative in popular and political discourse only with the rise of the relatively new-fangled discipline of psychology, which claimed intelligence for itself.
Plato conclude, in The Republic, that the ideal ruler is ‘the philosopher king’, as only a philosopher can work out the proper order of things. This idea was revolutionary at the time. Athens had already experimented with democracy, the rule of the people – but to count as one of those ‘people’ you just had to be a male citizen, not necessarily intelligent. Elsewhere, the governing classes were made up of inherited elites (aristocracy), or by those who believed they had received divine instruction (theocracy), or simply by the strongest (tyranny).
Plato’s novel idea fell on the eager ears of the intellectuals, including those of his pupil Aristotle. Aristotle was always the more practical, taxonomic kind of thinker. He took the notion of the primacy of reason and used it to establish what he believed was a natural social hierarchy.
So at the dawn of Western philosophy, we have intelligence identified with the European, educated, male human. It becomes an argument for his right to dominate women, the lower classes, uncivilised peoples and non-human animals. While Plato argued for the supremacy of reason and placed it within a rather ungainly utopia, only one generation later, Aristotle presents the rule of the thinking man as obvious and natural.
The late Australian philosopher and conservationist Val Plumwood has argued that the giants of Greek philosophy set up a series of linked dualisms that continue to inform our thought. Opposing categories such as intelligent/stupid, rational/emotional and mind/body are linked, implicitly or explicitly, to others such as male/female, civilised/primitive, and human/animal. These dualisms aren’t value-neutral, but fall within a broader dualism, as Aristotle makes clear: that of dominant/subordinate or master/slave. Together, they make relationships of domination, such as patriarchy or slavery, appear to be part of the natural order of things.
Descartes rendered nature literally mindless, and so devoid of intrinsic value – which thereby legitimated the guilt-free oppression of other species.
For Kant, only reasoning creatures had moral standing. Rational beings were to be called ‘persons’ and were ‘ends in themselves’. Beings that were not rational, on the other hand, had ‘only a relative value as means, and are therefore called things’. We could do with them what we liked.
This line of thinking was extended to become a core part of the logic of colonialism. The argument ran like this: non-white peoples were less intelligent; they were therefore unqualified to rule over themselves and their lands. It was therefore perfectly legitimate – even a duty, ‘the white man’s burden’ – to destroy their cultures and take their territory.
The same logic was applied to women, who were considered too flighty and sentimental to enjoy the privileges afforded to the ‘rational man’.
Galton believe that intellectual ability was hereditary and could be enhanced through selective breeding. He decided to find a way to scientifically identify the most able members of society and encourage them to breed – prolifically, and with each other. The less intellectually capable should be discouraged from reproducing, or indeed prevented, for the sake of the species. Thus eugenics and the intelligence test were born together.
From David Hume to Friedrich Nietzsche, and Sigmund Freud through to postmodernism, there are plenty of philosophical traditions that challenge the notion that we’re as intelligent as we’d like to believe, and that intelligence is the highest virtue.
From 2001: A Space Odyssey to the Terminator films, writers have fantasised about machines rising up against us. Now we can see why. If we’re used to believing that the top spots in society should go to the brainiest, then of course we should expect to be made redundant by bigger-brained robots and sent to the bottom of the heap.
Natural stupidity, rather than artificial intelligence, remains the greatest risk.
Dr. Scott Barry Kaufman When he was young, Kaufman had central auditory processing disorder, which made it hard for him to process verbal information in real time. He was asked to repeat third grade because he was considered a “slow” learner.
Kaufman thinks the traditional IQ test does a good job of measuring general cognitive ability, but says it misses all the ways that ability interacts with engagement. An individual’s goals within the learning classroom and excitement about a topic affect how he or she pursues learning, none of which is captured on IQ tests. Worse, those tests are often used to filter people in or out of special programs.
FOUR PRACTICES TO CULTIVATE CHILDREN’S CREATIVITY
allowing more solitary reflective time in kids’ schedules. Whether it’s the constant demands on attention at school or in after-school activities, there often isn’t enough time in a child’s day when she can switch off the executive functioning network and tap into the imagination network.
“We support obsessive passion, but not harmonious passion,” Kaufman said. He defines harmonious passion as a core part of people’s identity that makes them feel good about themselves. Harmonious passion is characterized by flexible engagement, where a child can abandon the pursuit if it isn’t paying dividends.
give young kids a diverse set of experiences in order to increase the chances of inspiration. “Lots of things add meaning to our lives,” he said.
educators, parents, and policymakers need to reset their mindsets around student ability. “Kids who think differently are not appreciated in our school system at all
it’s even worth measuring imagination, but Kaufman believes that measurement is important so researchers can see how changing behavior affects creative achievement. But he hopes the measurements are never used as another sorting mechanism.
My note: Kaufman makes a new call for an old trend. The futility of testing is raging across the United States K12 system. Higher education is turned into the last several decades (similarly to the United States health care system) into a cash cow. When the goal is profit, then good education goes down the drain. Cultivating children’s creativity cannot happen, when the foremost goals to make more money, which inevitably entails spending less cash (not only on teacher’s salaries).
bibliography on the impact of music on intellectual development.
Does music help learn better? get smarter? advance in life?
keywords: music, education, intelligence.
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Costa-Giomi, E. (2015). The Long-Term Effects of Childhood Music Instruction on Intelligence and General Cognitive Abilities. Update: Applications Of Research In Music Education, 33(2), 20-26.
Pelayo, J. M. G., & Galang, E. (2013). Social and Emotional Dynamics of College Students with Musical Intelligence and Musical Training: A Multiple Case Study. Retrieved from http://eric.ed.gov/?id=ED542664
Neves, V., Tarbet, V. (2007). Instrumental Music as Content Literacy Education: An Instructional Framework Based on the Continuous Improvement Process. Retrieved from http://eric.ed.gov/?id=ED499123
Conzelmann, K., & Süß, H. (2015). Auditory intelligence: Theoretical considerations and empirical findings. Learning And Individual Differences, 4027-40. doi:10.1016/j.lindif.2015.03.029
Juchniewicz, J. (2010). The Influence of Social Intelligence on Effective Music Teaching. Journal Of Research In Music Education, 58(3), 276-293.
Silvia, P. J., Thomas, K. S., Nusbaum, E. C., Beaty, R. E., & Hodges, D. A. (2016). How Does Music Training Predict Cognitive Abilities? A Bifactor Approach to Musical Expertise and Intelligence. Psychology Of Aesthetics, Creativity, And The Arts, doi:10.1037/aca0000058
Rickard, N. S., Bambrick, C. J., & Gill, A. (2012). Absence of Widespread Psychosocial and Cognitive Effects of School-Based Music Instruction in 10-13-Year-Old Students. International Journal Of Music Education, 30(1), 57-78.
Munsey, C. (2006). Music lessons may boost IQ and grades. American Psychological Association, 37(6), 13.
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Sharpe, N. N. (2014). The relationship between music instruction and academic achievement in mathematics. Dissertation Abstracts International Section A, 75.
keywords: music, education, multimedia.
Crappell, C., Jacklin, B., & Pratt, C. (2015). Using Multimedia To Enhance Lessons And Recitals. American Music Teacher, 64(6), 10-13.
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Ho, W.-C. (2007). Music Students’ Perception of the Use of Multi-Media Technology at the Graduate Level in Hong Kong Higher Education. Asia Pacific Education Review, 8(1), 12–26.
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Bolden, B. (2013). Learner-Created Podcasts: Students’ Stories with Music. Music Educators Journal, 100(1), 75-80.
The most popular approaches today focus on Big Data, ormimicking humansthat already know how to do some task. But sheer mimicry breaks down when one gives a machine new tasks, and,as I explained a few weeks ago, Big Data approaches tend to excel at finding correlations without necessarily being able to induce the rules of the game. If Big Data alone is not a powerful enough tool to induce a strategy in a complex but well-defined game like chess, then that’s a problem, since the real world is vastly more open-ended, and considerably more complicated.
Gardner now teaches at the Harvard Graduate School of Education. He is the author of numerous books on intelligence and creativity. His new book ”The App Generation,” co-authored with Katie Davis, explains how life for young people today is different than before the dawn of the digital age, and will be published on Oct. 22 by Yale University Press.
Gardner’s theory initially listed seven intelligences which work together: linguistic, logical-mathematical, musical, bodily-kinesthetic, spatial, interpersonal and intrapersonal; he later added an eighth, naturalist intelligence and says there may be a few more.
Excerpts from the blog entries under the article
The idea that multiple intelligences and learning styles have become interrelated is so true. Learning about all the different types of intelligences and learning, it can be hard to keep them all straight. This article was helpful in pointing out the differences. Educators should be aware of these differences, so that they might be able to better teach their students.
– how how human capacities are represented in the brain,
– a number of relatively independent mental faculties
– a number of relatively autonomous computers—[that compute] … information
A strong intelligence:
– an area where the person has considerable computational power
– the power of the mental computer, the intelligence, that acts upon that sensory information, once picked up
So “learning” = us[ing … (different) cognitive faculties?
Q1: Is that ok to assume and say?
Q2: What of “dimensions” – cognitive processing (higher order thinking) and knowledge (concrete to abstract) and sense of self, or affect[ive]?
– Individualize your teaching
– Pluralize your teaching. Teach important materials in several ways, …reach students who learn in different ways… [present] materials in various ways
Algorithmic test proctoring’s settings have discriminatory consequences across multiple identities and serious privacy implications.
While racist technology calibrated for white skin isn’t new (everything from photography to soap dispensers do this), we see it deployed through face detection and facial recognition used by algorithmic proctoring systems.
As Os Keyes has demonstrated, facial recognition has a terrible history with gender. This means that a software asking students to verify their identity is compromising for students who identify as trans, non-binary, or express their gender in ways counter to cis/heteronormativity.
These features and settings create a system of asymmetric surveillance and lack of accountability, things which have always created a risk for abuse and sexual harassment. Technologies like these have a long history of being abused, largely by heterosexual men at the expense of women’s bodies, privacy, and dignity.
my note: I am repeating this for years
Sean Michael Morris and Jesse Stommel’s ongoing critique of Turnitin, a plagiarism detection software, outlines exactly how this logic operates in ed-tech and higher education: 1) don’t trust students, 2) surveil them, 3) ignore the complexity of writing and citation, and 4) monetize the data.
Cheating is not a technological problem, but a social and pedagogical problem.
Our habit of believing that technology will solve pedagogical problems is endemic to narratives produced by the ed-tech community and, as Audrey Watters writes, is tied to the Silicon Valley culture that often funds it. Scholars have been dismantling the narrative of technological solutionism and neutrality for some time now. In her book “Algorithms of Oppression,” Safiya Umoja Noble demonstrates how the algorithms that are responsible for Google Search amplify and “reinforce oppressive social relationships and enact new modes of racial profiling.”
Anna Lauren Hoffmann, who coined the term “data violence” to describe the impact harmful technological systems have on people and how these systems retain the appearance of objectivity despite the disproportionate harm they inflict on marginalized communities.
Higher education is deeply complicit in the eugenics movement. Nazism borrowed many of its ideas about racial purity from the American school of eugenics, and universities were instrumental in supporting eugenics research by publishing copious literature on it, establishing endowed professorships, institutes, and scholarly societies that spearheaded eugenic research and propaganda.